A fresh viewpoint on drug discovery, pharma, and biotech

Mostapha Benhenda

I am the founder of Startcrowd, an online lab specialized in artificial intelligence. I got a PhD in mathematics from the university of Paris 13, and then I switched to artificial intelligence and machine learning, by educating myself with online courses. I founded Startcrowd with the idea that online education and social networks can disrupt the research landscape.

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Investments in artificial intelligence (AI) for drug discovery are surging. Big Pharmas are throwing big bucks at AI. Sanofi signed a 300 Million dollars deal with the Scottish AI startup Exscentia, and GSK did the same for 42 Million dollars. Also, the Silicon Valley VC firm Andreessen Horowitz launched a new 450 Million dollars bio investment fund, with one focus area in applications of AI to drug discovery.

In this craze, lots of pharma and biotech decision-makers wonder whether they should jump on the bandwagon, or wait and see.

Generative AI models in chemistry are increasingly popular in the research community, mainly, due to their interest for drug discovery applications. They generate virtual molecules with desired chemical and biological properties (more details in this blog post).

However, this flourishing literature still lacks a unified benchmark. Such benchmark would provide a common framework to evaluate and compare different generative models. Moreover, it would help to formulate best practices for this emerging industry of ‘AI molecule generators’: how much training data is needed, for how long the model should be trained, and so on.